{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de7a19e7-bebf-402d-87d5-328e0c4926ab",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 一、初始化环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17a70cda-7ee3-42cc-a7d8-e309b3ec727c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import math\n",
    "import random\n",
    "from pandas import read_csv as pdreadcsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98dda4fd-6d8e-44fc-a9de-1bfbccab87f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "plt.style.use('ggplot') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dea2c3bd-76ac-4f80-982f-43f9102d75d9",
   "metadata": {},
   "source": [
    "# 二、模拟数据展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d40df96-0ff0-4a60-a0d8-a8be8c78b004",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 展示一下如何使用plot绘图。这里是自己创造模拟数据。\n",
    "# 构建3个不同振幅和波长的数据（其中一个是直线）。\n",
    "def get_draw():\n",
    "    x = np.linspace(0, 50, 100)\n",
    "    ts1 = pd.Series(3.1 * np.sin(x / 1.5) + 3.5)\n",
    "    ts2 = pd.Series(2.2 * np.sin(x / 3.5 + 2.4) + 3.2)\n",
    "    ts3 = pd.Series(0.04 * x + 3.0)\n",
    "    ts1.plot()\n",
    "    ts2.plot()\n",
    "    ts3.plot()\n",
    "    plt.ylim(-2, 10)\n",
    "    plt.legend(['ts1', 'ts2', 'ts3'])\n",
    "    plt.show()\n",
    "\n",
    "# 计算欧拉距离\n",
    "    def euclid_dist(t1, t2):\n",
    "        return math.sqrt(sum((t1 - t2)**2))\n",
    "\n",
    "    print(\"ts1和ts2的欧拉距离：%f\" %euclid_dist(ts1, ts2))  # 26.959216\n",
    "    print(\"ts1和ts3的欧拉距离：%f\" %euclid_dist(ts1, ts3))  # 23.189249\n",
    "    print(\"ts2和ts3的欧拉距离：%f\" %euclid_dist(ts2, ts3))  # 17.298735"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd75cada-f527-41c5-9501-d8743d2cbf2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ts1和ts2的欧拉距离：26.959216\n",
      "ts1和ts3的欧拉距离：23.189249\n",
      "ts2和ts3的欧拉距离：17.298735\n"
     ]
    }
   ],
   "source": [
    "get_draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac89efe-ef79-4716-8ce6-c0ba5325e0ae",
   "metadata": {},
   "source": [
    "# 三、真实数据\n",
    "\n",
    "## 1、数据提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2343df93-b905-4037-a6b4-00979ce9fddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_wbcdata(filename):\n",
    "    df = pdreadcsv(filename)\n",
    "    workdata = df[[\"num\", \"days\", \"wbc\"]]\n",
    "\n",
    "    workdata_len = len(workdata[\"num\"])\n",
    "\n",
    "    days = [0] * (workdata_len)\n",
    "    WBC = [0] * (workdata_len)\n",
    "    Numb = [0] * (workdata_len)\n",
    "\n",
    "    for i in range(workdata_len):\n",
    "        Numb[i] = workdata[\"num\"][i]\n",
    "        days[i] = workdata[\"days\"][i]\n",
    "        WBC[i] = workdata[\"wbc\"][i]\n",
    "    s = int(len(WBC) / 30)\n",
    "\n",
    "    WBCData = np.mat(WBC).reshape(s, 30)\n",
    "    WBCData = np.array(WBCData)\n",
    "\n",
    "    return WBCData, Numb, days, WBC\n",
    "    # print(WBCData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ac09b33-995c-46ea-8595-1c5cc3330ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = r\"../data/20200315.csv\"\n",
    "WBCData, Numb, days, WBC = get_wbcdata(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b7c6353-2837-4b35-a2e7-19c1e0449e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.13, 3.65, 3.16, ..., 4.13, 4.13, 4.13],\n",
       "       [4.43, 4.45, 4.47, ..., 4.43, 4.43, 4.43],\n",
       "       [5.74, 5.52, 5.29, ..., 5.74, 5.74, 5.74],\n",
       "       ...,\n",
       "       [4.43, 4.52, 4.6 , ..., 4.43, 4.43, 4.43],\n",
       "       [3.55, 3.55, 3.55, ..., 1.77, 2.24, 2.14],\n",
       "       [6.21, 6.61, 7.02, ..., 6.21, 6.21, 6.21]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 白细胞计数分组数据\n",
    "WBCData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51f9afec-e856-406e-8fe1-4c7c13cd8309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(WBCData) # 共43个患者"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a227cf3-f53b-490f-b67a-0ca0a398bf23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "1290\n"
     ]
    }
   ],
   "source": [
    "print(type(Numb))\n",
    "print(len(Numb))  # 共1290条记录。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6b6b1b1-a762-4d25-9d73-0b9ea8bd2540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 所以每个患者共30条记录，也就是每天测白细胞技术，连续测30天。\n",
    "len(WBCData[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "61c44c27-2707-49d2-b1bf-c77f8f6eb957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6774, 6774, 6774, 6774, 6774, 6774, 6774, 6774, 6774, 6774]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numb是患者编号\n",
    "Numb[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e15f98b-bfc9-4c69-83d8-6fb29b416835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 时间序号，全部是是从1到30。\n",
    "days[:60:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "705c874d-67e2-4240-88e2-a7b15897ad56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1290"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1290条白细胞计数结果列表\n",
    "len(WBC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d7ea03-2d1b-41c5-a176-d253fecb4380",
   "metadata": {},
   "source": [
    "## 2、绘制白细胞计数结果的时序图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c3850a7c-2e6a-45ba-95de-328c4f6fc74c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_wbcdata(filename):\n",
    "    WBCData, _, _, _ = get_wbcdata(filename)\n",
    "\n",
    "    for every_wbcdata in WBCData:\n",
    "        plt.plot(every_wbcdata)\n",
    "        plt.legend(['WBC'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82ddba54-4773-49ec-98e9-343f9f860fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_wbcdata(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99571c3-cf32-489a-8b5e-91237c805790",
   "metadata": {},
   "source": [
    "## 3、定义相似距离\n",
    "\n",
    "### DTW距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ede317a-3cee-4ec3-8379-7bf070eb09a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DTW距离，时间复杂度为两个时间序列长度相乘\n",
    "def DTWDistance(s1, s2):\n",
    "    DTW = {}\n",
    "    s1_len = len(s1)\n",
    "    s2_len = len(s2)\n",
    "\n",
    "    \n",
    "# 先把路径矩阵设置为原始值，null。\n",
    "    for i in range(s1_len):\n",
    "        DTW[(i, -1)] = float('inf')\n",
    "\n",
    "    for i in range(s2_len):\n",
    "        DTW[(-1, i)] = float('inf')\n",
    "\n",
    "    DTW[(-1, -1)] = 0\n",
    "\n",
    "\n",
    "# 计算DTW距离\n",
    "    for i in range(s1_len):\n",
    "        for j in range(s2_len):\n",
    "            dist = (s1[i] - s2[j]) ** 2\n",
    "            DTW[(i, j)] = dist + min(DTW[(i - 1, j)], DTW[(i, j - 1)],\n",
    "                                     DTW[(i - 1, j - 1)])\n",
    "\n",
    "    return math.sqrt(DTW[len(s1) - 1, len(s2) - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bed8b9da-6f93-461e-ab09-77ee35f71c30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.342240802370898\n",
      "5.392355700433717\n",
      "6.0862632213863375\n",
      "2.7587134682674086\n",
      "4.213241032744269\n",
      "9.60509760491792\n",
      "18.969301515870324\n",
      "18.099455792923717\n",
      "17.013897260768914\n",
      "3.1652961946712033\n",
      "7.3719264782009315\n",
      "27.167025968994103\n",
      "10.641809056734669\n",
      "20.903906333506185\n",
      "18.846763647905178\n",
      "22.00403372111578\n",
      "12.653793897483869\n",
      "12.654631563186657\n",
      "8.845970834227295\n",
      "4.63326019126921\n",
      "9.152578871553088\n",
      "8.2858011079195\n",
      "4.6924194185942065\n",
      "4.604704116444401\n",
      "3.0147802573322005\n",
      "3.2875674898015412\n",
      "3.6629359808765427\n",
      "5.72729430010367\n",
      "1.610217376629626\n",
      "1.0540398474441077\n",
      "1.6183324751113415\n",
      "2.723215011709505\n",
      "3.379807686836635\n",
      "3.741082730975085\n",
      "10.699499988317209\n",
      "12.978509159375742\n",
      "11.29591076452005\n",
      "23.149043176770824\n",
      "13.445084603675797\n",
      "3.0016662039607263\n",
      "6.737180419136776\n",
      "21.77178219622822\n"
     ]
    }
   ],
   "source": [
    "for i in range(42):\n",
    "    print(DTWDistance(WBCData[i], WBCData[i+1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cfd155d-944d-402d-afa0-29c9abaa2cfc",
   "metadata": {},
   "source": [
    "上面只是相邻的患者数据进行了计算DTW距离，实际上是需要两两取数来计算，那就是 $\\mathrm{C}_{43}^2 = \\frac {43!}{2! \\times (43-2)! } = \\frac {43 \\times 42}{2} = 903 $ 次，$O(n^2)$ 级复杂度。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1945b8b-f04c-4093-9ec0-13300fc97a32",
   "metadata": {},
   "source": [
    "### DTW_W距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d567923d-1ab4-4f34-be6b-91594555b110",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DTW_W距离, 优化后的算法，只检测前W个窗口的值。\n",
    "def DTWDistance_W(s1, s2, w):\n",
    "    DTW = {}\n",
    "    s1_len = len(s1)\n",
    "    s2_len = len(s2)\n",
    "    w = max(w, abs(s1_len - s2_len))\n",
    "\n",
    "# 和DTW函数里不同，另一种初始化路径矩阵的循环方法。\n",
    "    for i in range(-1, s1_len):\n",
    "        for j in range(-1, s2_len):\n",
    "            DTW[(i, j)] = float('inf')\n",
    "    DTW[(-1, -1)] = 0\n",
    "\n",
    "    for i in range(s1_len):\n",
    "        for j in range(max(0, i - w), min(s2_len, i + w)):\n",
    "            dist = (s1[i] - s2[j]) ** 2\n",
    "            DTW[(i, j)] = dist + min(DTW[(i - 1, j)], DTW[(i, j - 1)],\n",
    "                                     DTW[(i - 1, j - 1)])\n",
    "    return math.sqrt(DTW[s1_len - 1, s2_len - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a0e0bf95-84b0-4fa8-b5a7-1ad1dbc8dc99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.342240802370898\n",
      "5.392355700433717\n",
      "6.0862632213863375\n",
      "2.7587134682674086\n",
      "4.213241032744269\n",
      "9.60509760491792\n",
      "18.969301515870324\n",
      "18.099455792923717\n",
      "17.013897260768914\n",
      "3.1652961946712033\n",
      "7.3719264782009315\n",
      "27.167025968994103\n",
      "10.641809056734669\n",
      "20.942886620521058\n",
      "18.846763647905178\n",
      "22.00403372111578\n",
      "12.653793897483869\n",
      "12.654631563186657\n",
      "8.845970834227295\n",
      "4.63326019126921\n",
      "9.152578871553088\n",
      "8.2858011079195\n",
      "4.6924194185942065\n",
      "4.604704116444401\n",
      "3.0147802573322005\n",
      "3.2875674898015412\n",
      "3.6629359808765427\n",
      "5.72729430010367\n",
      "1.610217376629626\n",
      "1.0540398474441077\n",
      "1.6183324751113415\n",
      "2.723215011709505\n",
      "3.379807686836635\n",
      "3.741082730975085\n",
      "10.70584419838062\n",
      "12.978509159375742\n",
      "11.29591076452005\n",
      "23.149043176770824\n",
      "13.445084603675797\n",
      "3.0016662039607263\n",
      "6.7889910885197065\n",
      "21.849725398732133\n"
     ]
    }
   ],
   "source": [
    "for i in range(42):\n",
    "    # 第三个位置的参数，可以设置从2到30，越接近30，计算结果越接近 DTWDistance()。\n",
    "    print(DTWDistance_W(WBCData[i], WBCData[i+1], 20))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e07ed29-1cd1-417b-ac3b-6542e96caa78",
   "metadata": {},
   "source": [
    "### LB_Keogh方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5faa5321-0c29-4419-bf84-298f3f78e8fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def LB_Keogh(s1, s2, r):\n",
    "    LB_sum = 0\n",
    "    for ind, i in enumerate(s1):\n",
    "        # 找出下界\n",
    "        lower_bound = min(s2[(ind - r if ind - r >= 0 else 0):(ind + r)])\n",
    "        # 找出上界\n",
    "        upper_bound = max(s2[(ind - r if ind - r >= 0 else 0):(ind + r)])\n",
    "        if i >= upper_bound:  # 在上界之上，减去上界\n",
    "            LB_sum = LB_sum + (i - upper_bound)**2\n",
    "        elif i < lower_bound: # 在下界\n",
    "            LB_sum = LB_sum + (i - lower_bound)**2\n",
    "    return math.sqrt(LB_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "70d03356-1624-45a7-aea3-0734199c20d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.677657967859635\n",
      "5.6532645436066415\n",
      "6.497030090741461\n",
      "2.765592160821981\n",
      "4.565183457430819\n",
      "9.259433027999068\n",
      "14.571194185789986\n",
      "18.11362746663406\n",
      "15.552793961214812\n",
      "4.160108171670539\n",
      "8.364986551094988\n",
      "24.506315512536766\n",
      "9.809113109756662\n",
      "21.521754110666723\n",
      "17.459029182632122\n",
      "21.88255469546461\n",
      "12.058042958954823\n",
      "12.38466794064338\n",
      "7.6189894343016356\n",
      "5.8201632279516025\n",
      "6.913197523577638\n",
      "9.196869032447944\n",
      "3.9035368577739846\n",
      "4.687461999845972\n",
      "1.4308389147629452\n",
      "4.381905977996334\n",
      "3.4566891673970352\n",
      "7.671231974070396\n",
      "3.3827799218985555\n",
      "3.7793650260328127\n",
      "2.962262648719724\n",
      "5.510571658185746\n",
      "4.604291476438042\n",
      "5.277925728920406\n",
      "11.431666545171792\n",
      "11.921048611594536\n",
      "13.277469638451446\n",
      "22.100459271245924\n",
      "12.68531828532497\n",
      "3.2523991144999407\n",
      "8.343626309944616\n",
      "21.73032673477323\n"
     ]
    }
   ],
   "source": [
    "for i in range(42):\n",
    "    print(LB_Keogh(WBCData[i], WBCData[i+1], 2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd55e227-a4f0-48b4-94fb-8b78d3a54d62",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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